Abertay University
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Fully Funded PhD Studentship in AI-Augmented Procedural Node-Graph Authoring for Technical Artists Abertay University in United Kingdom
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded for 3 years with a tax-free stipend of £21,805 per year (increasing in line with UKRI), tuition fees paid, and a generous study package including limited research consumables, travel budget, and training where appropriate. An extension of funding for a further 6 months is available if the candidate completes 70 hours of teaching per year during the initial 3 years.
Deadline
Oct 31, 2026
Country
United Kingdom
University
Abertay University
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About this position
Fully funded PhD studentship at Abertay University in Dundee, United Kingdom, focused on AI-augmented procedural node-graph authoring for technical artists.
The project explores how AI methods such as large language models, vision models, inverse procedural modelling, Bayesian optimisation, world models, and neural simulation can help draft editable procedural networks. The initial development environment is Houdini, with the aim of building a network builder that creates native node networks, sets initial parameters, and exposes meaningful artistic controls for technical artists to refine.
This is a PhD studentship funded by R-LINCS2. It offers a tax-free stipend of £21,805 per year, tuition fees paid, and a generous study package including research consumables, travel budget, and training support. Funding is available for 3 years, with a possible 6-month extension if teaching duties are completed.
Eligibility: applicants should have or expect a first-class or upper second-class honours degree in technical art, creative computing, computer science, AI, games technology, animation/VFX, or a related discipline. Experience in procedural/node-based production, AI/ML, or technical art is expected, along with strong Python programming or scripting skills. Houdini VEX and experience with procedural digital content creation tools are desirable. Non-native English speakers need IELTS 7.0 overall with no band below 6.5 (or equivalent).
Applications close on 31/10/2026. Apply through the Abertay University jobs page with a personal statement and CV. Shortlisted candidates will later complete an online Research Student Application Form and submit a research proposal. The project may align with CoSTAR and has applications in film, games, animation, and visual-effects production pipelines.
Funding details
Fully funded for 3 years with a tax-free stipend of £21,805 per year (increasing in line with UKRI), tuition fees paid, and a generous study package including limited research consumables, travel budget, and training where appropriate. An extension of funding for a further 6 months is available if the candidate completes 70 hours of teaching per year during the initial 3 years.
What's required
Applicants must have or expect to obtain a first-class or upper second-class honours degree in technical art, creative computing, computer science or AI, games technology, animation/VFX, or a related discipline. Candidates should have experience in at least one of procedural or node-based production, AI or machine learning, or technical art, plus demonstrable programming or scripting experience and the ability to develop and evaluate working software prototypes. Python is essential, Houdini VEX is relevant, and experience with Houdini or another procedural digital content creation environment is desirable. Applicants should be independent, enthusiastic, driven, able to undertake independent research, critically evaluate findings, and communicate technical and creative ideas clearly. Non-native English speakers must have IELTS 7.0 overall with no band below 6.5 or an equivalent Home Office-accepted qualification.
How to apply
Apply online via the Abertay University jobs page. Submit a personal statement explaining your interest in the project and a CV. If shortlisted, complete the online Research Student Application Form and include a research proposal. Contact Dr Paul Goodfellow for advice before applying.
More information can be found here
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